If you read the industry press, you can’t avoid the topic of robo advisors. Will they replace human advisors? If you can’t beat ‘em, should you join ‘em? How can a robo platform complement your practice? There are many questions and countless articles. While those questions are important to the overall conversation, one issue that hasn’t been widely addressed is the theoretical underpinnings. I’d like to address the topic – basically take a look “under the hood.”
At their core, robos are based on mean-variance optimization (MVO) the key to which is a portfolio variance formula that works like this in a two-asset example:
Portfolio Variance = [WEIGHT SQUARED OF ASSET 1] * [VARIANCE OF ASSET 1] +
[WEIGHT SQUARED OF ASSET 2] *[VARIANCE OF ASSET 2] +
2* [CORRELATION BETWEEN ASSET 1 & 2] *[STANDARD DEVIATION ASSET 1] *[STANDARD DEVIATION ASSET 2]
Since standard deviation is simply the square root of variance, all we really have are weights, variances and correlations.
There are three components of this formula – the first and second are the product of the asset’s squared weight and its variance and the third line is the product of the correlation between the assets and square roots of their variance. MVO is simply an algorithm that selects a set of weights for assets 1 and 2 such that the overall portfolio variance is as low as possible for a given return requirement. This technique has been widely used since the 1980s by large fund managers, including hedge funds.
While MVO is an important financial tool, let loose on individual investors it can be a disaster waiting to happen.
Why Optimizers Can Be “Error Maximizers”